7 citations · 8 across the 9 of their papers we have counts for
4 papers · 1 filter
On Approaches to Building Surrogate ODE Models for Diffusion Bridges
Maria Khilchuk, Vladimir Latypov, Pavel Kleshchev +1
Diffusion and Schrödinger Bridge models have established state-of-the-art performance in generative modeling but are often hampered by significant computational costs and complex t…
Towards Universal Neural Operators through Multiphysics Pretraining
Mikhail Masliaev, Dmitry Gusarov, Ilya Markov +1
Although neural operators are widely used in data-driven physical simulations, their training remains computationally expensive. Recent advances address this issue via downstream l…
Towards stable real-world equation discovery with assessing differentiating quality influence
Mikhail Masliaev, Ilya Markov, Alexander Hvatov
This paper explores the critical role of differentiation approaches for data-driven differential equation discovery. Accurate derivatives of the input data are essential for reliab…
On the balance between the training time and interpretability of neural ODE for time series modelling
Yakov Golovanev, Alexander Hvatov
Most machine learning methods are used as a black box for modelling. We may try to extract some knowledge from physics-based training methods, such as neural ODE (ordinary differen…